Note that the bounding box regression of the DOTA dataset is different from the conventional datasets, which makes object detection models more challenging. Specifically, in conventional object detection datasets, such as COCO [1]} or Pascal VOC [2]}, the ground truth bounding box of each object is annotated as \((x, y, w, h)\)  format. However, since objects have a wide variety of orientations in aerial images, the ground truth bounding box in the DOTA [3]} dataset is denoted as \((x, y, w, h, \theta )\)  format, with an additional rotation angle parameter \(\theta \) . To reflect such an angle in the object detection architecture, RoITransformer [4]} uses additional fully-connected layers to regress additional parameter \(\theta \)  on top of the Faster R-CNN architecture [5]}, which we follow in our experiments. For model tuning and evaluation on the DOTA dataset, we follow the conventional evaluation setups [4]}, [3]}, [8]}. We first tune the hyperparameter of our UAGDet on the \(val\)  dataset while training the model on the \(train\)  set. After the tuning is done, we use both the \(train/val\)  sets to train the model, and infer on the \(test\)  set. Inference results on the \(test\)  set is uploaded to the DOTA evaluation server [3]}, [8]}, to measure the final performance. For evaluation metrics, we use the Average Precision for each class result, and also the mAP for all results [3]}.
